Service · Data, Analytics & Experimentation

Data, Analytics & Experimentation

Data, analytics and experimentation is the practice of instrumenting a business so every growth decision can be measured. We build the event tracking, warehouse, attribution models, dashboards, and testing program that turn scattered channel data into a real-time picture of what is working.

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What this includes

6 deliverables

Event instrumentation across web, product, and ad platforms

Cloud data warehouse (BigQuery) with modeled tables

Reporting layer in Looker or an equivalent

Cross-channel attribution and marketing mix modeling

A/B and holdout testing program with sample-size discipline

Unit economics: CAC, payback, LTV, contribution margin

What it's for

Outcomes

Attribution clarity

CAC reduction

CRO uplift

Better forecasting

Pipeline velocity

Margin visibility

How we work

A four-phase engagement, run like an operator.

Part of the Closed-Loop Growth System →

Typical: 6-week onboarding

01
Audit
Full audit of current tracking, dashboards, and attribution logic.
02
Design
Event dictionary, warehouse schema, and reporting spec.
03
Build
Instrumentation shipped, warehouse loaded, reports wired up.
04
Run
Experiment cadence stood up. Insights delivered weekly.

Proof

Full measurement stack in six months.

For a LegitScript-certified telehealth platform we built the event dictionary, warehouse, attribution model, and reporting layer from scratch, then used it to shift paid budget toward channels that were actually driving qualified visits.

Result · Anonymized under NDA

50% lift

Lead-to-order conversion 8% to 12% over 12 months at a regulated services platform. Validated at 99% confidence.

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lifecycle

Questions

Frequently asked.

Do we need a data engineer on our team?

No. We deliver the warehouse, models, and reporting layer, and hand off documentation. Most clients keep us on for ongoing analytics work rather than hire in house.

Which attribution model do you use?

It depends on the business. For mid-funnel SaaS we typically combine multi-touch attribution with a holdout-based validation. For paid-heavy consumer health we build a marketing mix model.

How long until we see reliable numbers?

Instrumentation is usually reliable within four to six weeks. Attribution models take another four to eight weeks of data to stabilize.

Let's look at your numbers.

A no-pressure 30-minute review. You leave with two or three findings.

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